1. Technical Field
One or more embodiments of the present invention relate generally to digital object classification. More specifically, one or more embodiments of the present invention relate to generating normalized classification scores for digital images, electronic documents, and other digital content.
2. Background and Relevant Art
Individuals and businesses increasingly rely on computing devices to identify and classify digital objects. For example, in light of the recent proliferation of digital cameras in handheld devices, individuals now commonly manage personal digital image collections containing thousands of digital images. Many individuals utilize computing devices to classify these digital images based on, for example, the contents (e.g., a particular individual, pet, item, or location, etc.) of the digital images. Similarly, businesses and individuals commonly utilize computing devices to classify digital files, digital content (e.g., advertisements or other digital content for users), and other digital objects in a variety of ways.
Conventional digital classification systems operate by generating classification scores to classify objects using one or more classification models. These conventional systems typically train the classification models using training data, such as digital objects that have been tagged or otherwise labeled with one or more known classifications. Using the trained models, conventional digital classification systems generate classification scores and then use the classification scores to classify digital objects. For example, with regard to identifying objects in an image, common digital classification systems can generate classification scores with regard to the likelihood that an object portrayed in a digital image corresponds to a known object (e.g., a known person) from other images (e.g., images used to train the classification models). Common classification systems can then compare the generated classifications scores (to other classification scores and/or to a threshold) to identify the object portrayed in the digital image.
Although conventional classification systems have proven very useful, they still suffer from a number of shortcomings. For instance, conventional classification systems often have difficulty accurately classifying digital objects with regard to multiple possible classifications and multiple corresponding classification scores. As one example, and as will be described in greater detail below, some common classification systems can have difficulty accurately utilizing multiple classification scores to identify whether an unknown object in an image corresponds to a first object, a second object, or some other object. Indeed, in many conventional systems, generating and comparing multiple classification scores to classify a digital object leads to inaccurate classifications, even where users attempt to provide additional information (i.e., training data) to generate more accurate classification models.
Accordingly, there is much to be considered in terms of accurately classifying digital objects.